PAFit (version 0.9.6)

plot.PAFit_result:

Description

This function plots the estimated attachment function \(A_k\) and node fitness \(eta_i\), together with additional information such as their confidence intervals or the estimated attachment exponent (\(\alpha\) when assuming \(A_k = k^\alpha\)).

Usage

# S3 method for PAFit_result
plot(x,
     net_stat              ,
     true_f      = NULL    , plot             = "A"   , plot_bin     = TRUE             ,
     line        = FALSE   , confidence       = TRUE  , high_deg     = 1                , 
     shade_point = 0.5     , shade_interval   = 0.5   , col_interval = "lightsteelblue" ,
     col_point   = "black" , label_x          = NULL  , label_y      = NULL             ,
     max_A       = NULL    , min_A            = NULL  , f_min        = NULL             , 
     f_max       = NULL    , plot_true_degree = FALSE , 
     ...)

Arguments

x

An object of class "PAFit_result", containing the result

net_stat

An object of class "PAFit_data", containing the summerized statistics.

true_f

Vector. Optional parameter for the true value of node fitnesses (only available in simulated datasets). If this parameter is specified and plot == "true_f", a plot of estimated \(\eta\) versus true \(\eta\) is produced (after a suitable rescaling of the estimated \(f\)).

plot

String. Indicates which plot is produced.if \("A"\) then PA function is plotted. If \("f"\) then estimated fitness is plotted. If \("true_f"\) then estimated fitness and true fitness are plotted together (require supplement of true fitness). Default value is \("A"\).

plot_bin

Logical. If TRUE then only the center of each bin is plotted. Default is \(TRUE\).

line

Logical. Indicates whether to plot the line fitted from the log-linear model or not. Default value is \(TRUE\).

confidence

Logical. Indicates whether to plot the confidence intervals of \(A_k\) and \(eta_i\) or not. If confidence == TRUE, a 2-sigma confidence interval will be plotted at each \(A_k\) and \(eta_i\).

high_deg

Integer. If plot == "A", the estimated PA function is plotted starting from high_deg, and \(A_{high_deg}\) is normalized to 1. If plot == "f" or plot == "true_f", only nodes whose number of edges acquired is not less than \(high_deg\) are plotted. Default value is 1.

shade_point

Numeric. Value between 0 and 1. This is the transparency level of the points. Default value is \(0.5\).

shade_interval

Numeric. Value between 0 and 1. This is the transparency level of the confidence intervals. Default value is \(0.5\).

max_A

Numeric. Specify the maximum of the axis of PA.

min_A

Numeric. Specify the minimum of the axis of PA.

f_min

Numeric. Specify the minimum of the axis of fitness.

f_max

Numeric. Specify the maximum of the axis of fitness.

plot_true_degree

Logical. The degree of each node is plotted or not.

label_x

String. The label of x-axis.

label_y

String. The label of y-axis.

col_interval

String. The name of the color of the confidence intervals. Default value is \("lightsteelblue"\).

col_point

String. The name of the color of the points. Default value is \("black"\).

…

Value

Outputs the desired plot.

References

1. Pham, T., Sheridan, P. & Shimodaira, H. (2016). Nonparametric Estimation of the Preferential Attachment Function in Complex Networks: Evidence of Deviations from Log Linearity, Proceedings of ECCS 2014, 141-153 (Springer International Publishing) (http://dx.doi.org/10.1007/978-3-319-29228-1_13). 2. Pham, T., Sheridan, P. & Shimodaira, H. (2015). PAFit: A Statistical Method for Measuring Preferential Attachment in Temporal Complex Networks. PLoS ONE 10(9): e0137796. doi:10.1371/journal.pone.0137796 (http://dx.doi.org/10.1371/journal.pone.0137796). 3. Pham, T., Sheridan, P. & Shimodaira, H. (2016). Joint Estimation of Preferential Attachment and Node Fitness in Growing Complex Networks. Scientific Reports 6, Article number: 32558. doi:10.1038/srep32558 (www.nature.com/articles/srep32558).

Examples

library("PAFit")
net        <- GenerateNet(N = 50 , m = 1 , mode = 1 , alpha = 1 , shape = 10 , rate = 10)
net_stats  <- GetStatistics(net$graph)
result     <- PAFit(net_stats)
#plot A
plot(result , net_stats , plot = "A")
#plot f
plot(result , net_stats , plot = "f")
#plot true_f
plot(result , net_stats , net$fitness, plot = "true_f")